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A Comparative Study of ML Models on Large-Scale Turkish Sentiment Datasets

A Comparative Study of ML Models on Large-Scale Turkish Sent...

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Innovations in Intelligent systems and Applications Conference

作者： Rukiye Savran Kiziltepe Ercan Ezin Murat Karakus Dept. of Software Engineering Karadeniz Tech. University Trabzon Türkiye Dept. of Computer Engineering Harran University Sanliurfa Türkiye Dept. of Software Engineering Ankara University Ankara Türkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

This study investigates the performance of traditional Machine Learning (ML) models for Sentiment Analysis (SA) in the Turkish language, addressing the growing need for robust Natural Language Processing (NLP) techniques in low-resource languages. We compare the efficacy of Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers on two recently developed large-scale Turkish sentiment datasets: “Vitamins and Supplements Customer Review” (VSCR) and “Turkish Sentiment Analysis version I” (TRSAv1). Our research leverages advanced hyperparameter tuning techniques to optimize these models, providing benchmarks and practical insights for Turkish SA. The study contributes to the field by establishing performance baselines on new open-source datasets, enhancing understanding of NLP models for agglutinative languages, and analyzing factors affecting classifier performance in binary and multi-class scenarios. Our findings reveal the strengths and limitations of each model within the context of Turkish language SA, offering valuable guidance for future research and applications in this domain.

关键词： Support vector machines Analytical models Sentiment analysis Technological innovation Systematics Computational modeling Linguistics Benchmark testing Tuning Classification tree analysis

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone 6

An Investigation of Running Load Comparisons of ARCore on Na...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Brata, Komang Candra Funabiki, Nobuo Sukaridhoto, Sritrusta Fajrianti, Evianita Dewi Mentari, Mustika Okayama University Dept. of Information and Communication Systems Okayama Japan Universitas Brawijaya Dept. of Informatics Engineering Malang Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Informatic and Computer Surabaya Indonesia State Polytechnic of Malang Dept. of Information Technology Malang Indonesia

ISBN: (纸本)9798350326642

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-Time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements. © 2023 IEEE.

关键词： Augmented reality

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

引用

4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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Towards An Automatic Construction Of Simulation Scenarios: A Systematic Review

Towards An Automatic Construction Of Simulation Scenarios: A...

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2023 Winter Simulation Conference, WSC 2023

作者： Davis, Christopher W.H. Jetter, Antonie J. Giabbanelli, Philippe J. Microsoft 1414 NW Northrup PortlandOR97209 United States Portland State University Dept. of Engineering & Technology Management PortlandOR97201 United States Miami University Dept. of Computer Science & Software Engineering OxfordOH45056 United States

ISBN: (纸本)9798350369663

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of 'what-if' scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods. © 2023 IEEE.

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3D Face Reconstruction with Graph Attention Based on Action Unit Features

3D Face Reconstruction with Graph Attention Based on Action ...

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International Conference on Information Networking

作者： Hyeonjin Kim Hyukjoon Lee Jinheung Kong Dept. of Computer Engineering Kwangwoon University Seoul Korea Dept. of Embedded Software Enngeeing Kwangwoon University Seoul Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

The reconstruction of 3D face shapes and expressions from 2D images remains unconquered due to the lack of detailed modeling of human facial movement based on the correlation between the different parts of faces. We propose to use facial action units (AUs), which are detailed taxonomy of the human facial movements based on observation of activation of muscles or muscle groups, in the 3D facial expression reconstruction to allow for detailed modeling of various facial expression types. We present a novel 3D face reconstruction framework called Action Unit feature-based Graph Attention Network Encoder (AUGANE) that can generate a 3D face model that is responsive to AU activation given a single monocular 2D image to capture expressions. AUGANE leverages AU-specific features as well as facial global features to enable accurate 3D facial expression reconstruction with Graph Attention Network Encoder (GANE). We also introduce a novel loss function which is to force learning toward the minimal discrepancy in AU activation between the input and rendered reconstruction. The experimental results demonstrate the superior performance of the proposed framework over state-of-the-art methods.

关键词： Solid modeling Gold Three-dimensional displays Shape Taxonomy Force Muscles Transformers Image reconstruction Faces

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Validation and evaluation of models in the study of characteristic descriptors based on patients' gender

Validation and evaluation of models in the study of characte...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Marinova, Ginka Todorova, Maya Technical University of Varna Dept. of Computer Science and Engineering Varna Bulgaria Technical University of Varna Dept. of Software and Internet Technologies Varna Bulgaria

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

Cancer-related distress is the result of the psyche's reaction to the diagnosis given and to the various stressors in the development of the type of malignant disease. The spreading of the psychological distress and depression may change depending on the stage, gender, and age of the patient. Identifying gender-based distress is important for improving the work of health professionals. The results of the study of models of characteristic descriptors based on patients' sex evaluated distress by applying three methods of machine learning. This report evaluates the effectiveness of the metrics accuracy and classification error. It implemented validation of models of descriptors and the calculated correlation coefficients. The results obtained for the two metrics - accuracy and classification error - were high. © 2022 IEEE.

关键词： Machine learning

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Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices 8

Deep Convolutional Neural Network Based Covid-19 Classificat...

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8th International Conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Bhosale, Yogesh H. Zanwar, Shrinivas Ahmed, Zakee Nakrani, Mahendra Bhuyar, Devendra Shinde, Ulhas Dept. of Computer Science and Engineering Mesra Ranchi India Chh. Shahu College of Engineering Dept. of Artificial Intelligence and Data Science Csmss Aurangabad India WeAgile Software Solution Pvt. Ltd Dept. of Computer Vision Engineering Pune India

ISBN: (纸本)9781665408165

The Coronavirus Disease 2019 (COVID19) epidemic, which erupted at the end of 2019, continued rapidly throughout the nations from Wuhan, China. This highly contagious infectious disease is rapidly spreading among the public. Early research on COVID-19-affected patients has revealed distinctive anomalies in chest radiography images. As a result, it is now necessary to identify various risk factors that can move an infected person from a mild to a serious stage of sickness. In Deep Learning (DL), strategies as a subset of Artificial Intelligence (AI) are used to deal with many real-life glitches. This paper introduces a Deep Convolutional Neural Network (DCNN) to perform multiclass classification for COVID-19, Pneumonia, and Normal Patients from radiological imaging of the chest. Also, the work is implemented with an IoT framework, used for communicating user and DCNN model. This Deep Convolutional Neural Network (DCNN) classification mechanism achieved a perfect test accuracy of 94.95% for COVID-19. The used datasets are acquired from Kaggle and GitHub. © 2022 IEEE.

关键词： COVID-19

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Assessing Interaction Mechanics in Extended Reality for Control engineering Applications

Assessing Interaction Mechanics in Extended Reality for Cont...

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International Conference on software, Telecommunications and computer Networks (SoftCOM)

作者： Armin Remenyi Saleh Alsaleh Aleksei Tepljakov Dept. of Software Science Tallinn University of Technology Tallinn Estonia Dept. of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

This research paper delves into the evaluation of human-machine interactions within the realm of Extended Reality (XR) applications, focusing on the integration of Digital Twins (DT) for control systems. The research explores multiple XR interaction methods and assesses their efficacy in enhancing user experience and operational efficiency in industrial settings, specifically in control systems. The study employs advanced tools such as the Microsoft HoloLens 2 headset, Unreal Engine 5, and the Mixed Reality Toolkit (MRTK) to develop and test XR interaction methods. Data collection and analysis involves usability testing using the System Usability Scale (SUS) and the NASA Task Load Index (NASA-TLX) to measure cognitive, physical and temporal demands on users. The findings highlight the most effective XR interaction strategies, providing valuable insight for future research and practical implementations in intelligent manufacturing environments.

关键词： Technological innovation Control engineering Extended reality NASA User experience software Digital twins Telecommunications Usability Testing

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Methodology for the Study of Defects in Technical Structures at Construction Sites 23

Methodology for the Study of Defects in Technical Structures...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Snitsar, Liliya R. Iskakov, Kazizat T. Zhartybaeva, Makpal G. Suleimenov, Bakhytzhan K. Boranbaev, Samat A. L.N. Gumilyov Eurasian National University Dept. of Computer and Software Engineering Nur-Sultan Kazakhstan

ISBN: (纸本)9781665498043

The article deals with the problem of analyzing existing approaches to the survey of construction sites. It is given an explanation of the relevance of the study of objects by non-destructive diagnostic methods. In practice, the use of one of the methods of non-destructive testing of construction objects - the ultrasonic method is shown. The mathematical approach to solving the problems is also described in the article, which presents the basic formulas used in solving the problem. Authors also characterized the available non-destructive testing devices and their technical characteristics. The results and algorithms of various methods for solving the problem are presented. An experimental study is described using one of the considered devices. The process of preparation for measurements and installation of sensors is described in detail. There were given the results of experimental studies in laboratory conditions and on the construction site. According to the results of the experiments, conclusions were made for each of them. © 2022 IEEE.

关键词： Nondestructive examination

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Sumble, Malaika Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Fernandez-Rojas, Raul University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences & Tech. Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles. © 2024 IEEE.

关键词： Drones

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